Estimação em Pequenos Domínios com modelos que combinam Informação Seccional e Cronológica

O objectivo principal deste artigo consiste na proposta de um novo estimador para parâmetros de interesse em pequenos domínios com dados de nível área.

Relative contributions of light interception and radiation use efficiency to the reduction of maize productivity under cold temperatures

Maize (Zea mays L.) is a chill-susceptible crop cultivated in northern latitude environments. The detrimental effects of cold on growth and photosynthetic activity have long been established. However, a general overview of how important these processes are with respect to the reduction of productivity reported in the field is still lacking. In this study, a model-assisted approach was used to dissect variations in productivity under suboptimal temperatures and quantify the relative contributions of light interception (PARc) and radiation use efficiency (RUE) from emergence to flowering. A combination of architectural and light transfer models was used to calculate light interception in three field experiments with two cold-tolerant lines and at two sowing dates. Model assessment confirmed that the approach was suitable to infer light interception. Biomass production was strongly affected by early sowings. RUE was identified as the main cause of biomass reduction during cold events. Furthermore, PARc explained most of the variability observed at flowering, its relative contributions being more or less important according to the climate experienced. Cold temperatures resulted in lower PARc, mainly because final leaf length and width were significantly reduced for all leaves emerging after the first cold occurrence. These results confirm that virtual plants can be useful as fine phenotyping tools. A scheme of action of cold on leaf expansion, light interception and radiation use efficiency is discussed with a view towards helping breeders define relevant selection criteria. This paper originates from a presentation at the 5th International Workshop on Functional–Structural Plant Models, Napier, New Zealand, November 2007.

Generalised regression estimation for domain class frequencies

This study examines the properties of Generalised Regression (GREG) estimators for domain class frequencies and proportions. The family of GREG estimators forms the class of design-based model-assisted estimators. All GREG estimators utilise auxiliary information via modelling. The classic GREG estimator with a linear fixed effects assisting model (GREG-lin) is one example. But when estimating class frequencies, the study variable is binary or polytomous. Therefore logistic-type assisting models (e.g. logistic or probit model) should be preferred over the linear one. However, other GREG estimators than GREG-lin are rarely used, and knowledge about their properties is limited. This study examines the properties of L-GREG estimators, which are GREG estimators with fixed-effects logistic-type models. Three research questions are addressed. First, I study whether and when L-GREG estimators are more accurate than GREG-lin. Theoretical results and Monte Carlo experiments which cover both equal and unequal probability sampling designs and a wide variety of model formulations show that in standard situations, the difference between L-GREG and GREG-lin is small. But in the case of a strong assisting model, two interesting situations arise: if the domain sample size is reasonably large, L-GREG is more accurate than GREG-lin, and if the domain sample size is very small, estimation of assisting model parameters may be inaccurate, resulting in bias for L-GREG. Second, I study variance estimation for the L-GREG estimators. The standard variance estimator (S) for all GREG estimators resembles the Sen-Yates-Grundy variance estimator, but it is a double sum of prediction errors, not of the observed values of the study variable. Monte Carlo experiments show that S underestimates the variance of L-GREG especially if the domain sample size is minor, or if the assisting model is strong. Third, since the standard variance estimator S often fails for the L-GREG estimators, I propose a new augmented variance estimator (A). The difference between S and the new estimator A is that the latter takes into account the difference between the sample fit model and the census fit model. In Monte Carlo experiments, the new estimator A outperformed the standard estimator S in terms of bias, root mean square error and coverage rate. Thus the new estimator provides a good alternative to the standard estimator.

Dendrites Inhibition in Rechargeable Lithium Metal Batteries

The specific high energy and power capacities of rechargeable lithium metal (Li⁰) batteries are ideally suited to portable devices and are valuable as storage units for intermittent renewable energy sources. Lithium, the lightest and most electropositive metal, would be the optimal anode material for rechargeable batteries if it were not for the fact that such devices fail unexpectedly by short-circuiting via the dendrites that grow across electrodes upon recharging. This phenomenon poses a major safety issue because it triggers a series of adverse events that start with overheating, potentially followed by the thermal decomposition and ultimately the ignition of the organic solvents used in such devices.

In this thesis, we developed experimental platform for monitoring and quantifying the dendrite populations grown in a Li battery prototype upon charging under various conditions. We explored the effects of pulse charging in the kHz range and temperature on dendrite growth, and also on loss capacity into detached “dead” lithium particles.

Simultaneously, we developed a computational framework for understanding the dynamics of dendrite propagation. The coarse-grained Monte Carlo model assisted us in the interpretation of pulsing experiments, whereas MD calculations provided insights into the mechanism of dendrites thermal relaxation. We also developed a computational framework for measuring the dead lithium crystals from the experimental images.

水稻分蘖逆制的可塑性生长发育试验和模型辅助分析: 应用植物生长模型缩减基因型与表现型的间隙

表现型是基因型和环境相互作用的结果，不同环境条件下给定基因型能表达为不同的表型，这是我们所熟知的植物表型可塑性。可塑性一方面帮助植物更好地适应不利环境，但我们也不得不承认可塑性，使得人们难以从表型直接理解基因功能。如今，基因组学快速发展允许解密基因更迅速便捷，甚至发现大量基因。因此，进一步理解可塑性过程的基因背景、理解基因和环境对表型的作用非常必要。由于从基因到表型非线性过程，从而引起基因型和表型差异，期望有效方法或工具能跨越这个横沟。植物生长模型已被开发用来模拟植物响应环境动态关系，并且将参数和环境整合到模型方法中。因此，普遍认为植物生长模型将在探讨复杂可塑性基因功能扮演重要作用。水稻是普遍应用在基因组学和功能基因组学典型的模式植物。水稻分蘖是重要的基因依赖环境敏感的过程，这是农学上非常关注的现象。本文将应用模型方法理解水稻分蘖逆制的可塑性。本研究设计了一个相对优化环境条件下，野生型水稻分蘖逆制试验，该试验有两个处理（1）手工剪切分蘖;（2）一个TDNA突变体，并分别设置对照。本试验在法国国际农业研究发展中心（CIRAD）温室开展，每个试验利用水培方法，培育植株50天左右（营养生长阶段）。在营养生长阶段，定期破坏性测量单个器官的鲜重、干重和单个器官的大小。本文尝试应用两个植物生长模型模拟和解释水稻响应分蘖逆制表型发育。GreenLab是一个植物结构数学模型，已被开发用来模拟植物结构动态和结构功能反馈。植物3D结构决定光捕获和生物产量，然后，生物量分配到新的器官，因此，器官形态结构将发生变化，新阶段的生物量生产将会更新。通过基于最小二乘法的CornerFit软件实现了模型参数优化。另一个模型EcoMeristem，基于作物模型和形态发生概念，用来模拟水稻分生组织活动、器官发生和形态过程等可塑性过程，内部竞争指数Ic主要与环境相关，参数主要描述基因功能。通过植物生长过程模拟与测量的优化，手工提取了模型参数。这两个植物生长模型演示了缩减基因型与表型之间的差距，并实现了水稻响应分蘖完全逆制的可塑性过程。GreenLab模型有一个极好的器官发生基础，但本研究限于单茎拓扑结构。另外，该模型有更长的时间步长，这对描述植物可塑性没有提供足够的分辨能力，这在EcoMeristem模型中得到了解决。很明显，EcoMeristem模型有更弱的结构基础，这可能蕴含了一些可塑性信息的缺失。总体而言，EcoMeristem模型有更专业的可塑性过程、基因环境理解和表达能力。

Small Area Estimation of the Mean Price of the Habitation Transaction in Portugal: Methodological and Practical Issues

The Portuguese National Statistical Institute intends to produce estimations for the mean price of the habitation transation.

Diagnóstico do nível de maturidade do processo de desenvolvimento de produtos: um estudo de caso em empresa calçadista

The process of product development (PPD) is a strategic factor in which companies seek to identify consumer needs. The relationship of the practices adopted, tools, techniques, among others, can determinate the maturity level of the company in this process. The purpose of this paper is to understand and diagnose the level of maturity of the PDP in footwear segment industry. For the case study, a company inserted into one of the largest poles of women’s footwear in Brazil, located in Jaú, São Paulo State, was researched. The diagnosis is based on the maturity levels of the unified model, reference in the process of product development, proposed by Rozenfeld et al. (2006). The application of the model assisted to diagnose the current maturity level of the company in this process, providing useful information to achieve higher levels of maturity in the PDP.

Parallel Problem Solving from Nature – PPSN XIV:14th International Conference, Edinburgh, UK, September 17-21, 2016, Proceedings

This book constitutes the refereed proceedings of the 14th International Conference on Parallel Problem Solving from Nature, PPSN 2016, held in Edinburgh, UK, in September 2016. The total of 93 revised full papers were carefully reviewed and selected from 224 submissions. The meeting began with four workshops which offered an ideal opportunity to explore specific topics in intelligent transportation Workshop, landscape-aware heuristic search, natural computing in scheduling and timetabling, and advances in multi-modal optimization. PPSN XIV also included sixteen free tutorials to give us all the opportunity to learn about new aspects: gray box optimization in theory; theory of evolutionary computation; graph-based and cartesian genetic programming; theory of parallel evolutionary algorithms; promoting diversity in evolutionary optimization: why and how; evolutionary multi-objective optimization; intelligent systems for smart cities; advances on multi-modal optimization; evolutionary computation in cryptography; evolutionary robotics - a practical guide to experiment with real hardware; evolutionary algorithms and hyper-heuristics; a bridge between optimization over manifolds and evolutionary computation; implementing evolutionary algorithms in the cloud; the attainment function approach to performance evaluation in EMO; runtime analysis of evolutionary algorithms: basic introduction; meta-model assisted (evolutionary) optimization. The papers are organized in topical sections on adaption, self-adaption and parameter tuning; differential evolution and swarm intelligence; dynamic, uncertain and constrained environments; genetic programming; multi-objective, many-objective and multi-level optimization; parallel algorithms and hardware issues; real-word applications and modeling; theory; diversity and landscape analysis.

Modelagem computacional aplicada na estimação de parâmetros cinéticos em processos químicos e cromatográficos

A estimação de parâmetros cinéticos em processos químicos e cromatográficos utilizando técnicas numéricas assistidas por computadores tem conduzido para melhoria da eficiência e o favorecimento da compreensão das fenomenologias envolvidas nos mesmos. Na primeira parte deste trabalho será realizada a modelagem computacional do processo de produção de biodiesel via esterificação, sendo que, o método de otimização estocástica Random Restricted Window (R2W) será correlacionado com os dados experimentais da produção de biodiesel a partir da esterificação do ácido láurico com etanol anidro na presença do catalisador ácido nióbico (Nb2O5). Na segunda parte do mesmo será realizada a modelagem computacional do processo de cromatografia de adsorção (batch process) onde serão correlacionados os dados provenientes dos modelos cinéticos de HASHIM, CHASE e IKM2 com os dados experimentais da adsorção de amoxicilina com quitosana, e também serão correlacionados os dados experimentais da adsorção de Bovine Serum Albumin (BSA) com Streamline DEAE com os dados provenientes de uma nova aplicação do método R2W mediante a implementação de um modelo cinético reversível. Ademais, as constantes cinéticas para cada processo supracitado serão estimadas levando em consideração o valor mínimo da função resíduos quadrados.

Long range exhaustion-a mathematical model for the axisymmetric air flow of a local exhaust ventilation hood assisted by a turbulent radial jet

Abstract-Mathematical modelling techniques are used to predict the axisymmetric air flow pattern developed by a state-of-the-art Banged exhaust hood which is reinforced by a turbulent radial jet flow. The high Reynolds number modelling techniques adopted allow the complexity of determining the hood's air Bow to be reduced and provide a means of identifying and assessing the various parameters that control the air Bow. The mathematical model is formulated in terms of the Stokes steam function, ψ, and the governing equations of fluid motion are solved using finite-difference techniques. The injection flow of the exhaust hood is modelled as a turbulent radial jet and the entrained Bow is assumed to be an inviscid potential flow. Comparisons made between contours of constant air speed and centre-line air speeds deduced from the model and all the available experimental data show good agreement over a wide range of typical operating conditions. | Mathematical modelling techniques are used to predict the axisymmetric air flow pattern developed by a state-of-the-art flanged exhaust hood which is reinforced by a turbulent radial jet flow. The high Reynolds number modelling techniques adopted allow the complexity of determining the hood's air flow to be reduced and provide a means of identifying and assessing the various parameters that control the air flow. The mathematical model is formulated in terms of the Stokes steam function, Ψ, and the governing equations of fluid motion are solved using finite-difference techniques. The injection flow of the exhaust hood is modelled as a turbulent radial jet and the entrained flow is assumed to be an inviscid potential flow. Comparisons made between contours of constant air speed and centre-line air speeds deduced from the model and all the available experimental data show good agreement over a wide range of typical operating conditions.

CAMCR: Computer assisted mixture model analysis for capture-recapture count data

Population size estimation with discrete or nonparametric mixture models is considered, and reliable ways of construction of the nonparametric mixture model estimator are reviewed and set into perspective. Construction of the maximum likelihood estimator of the mixing distribution is done for any number of components up to the global nonparametric maximum likelihood bound using the EM algorithm. In addition, the estimators of Chao and Zelterman are considered with some generalisations of Zelterman’s estimator. All computations are done with CAMCR, a special software developed for population size estimation with mixture models. Several examples and data sets are discussed and the estimators illustrated. Problems using the mixture model-based estimators are highlighted.